LIBSVM简易入门中英对照.docx

1、LIBSVM简易入门中英对照View this tutorial in: English Only TraditionalChinese Only Both (Default) (req. JavaScript if you want to switch languages) Core StyleSheets: Chocolate Midnight Modernist Oldstyle Steely Swiss Traditional Ultramarine Core StyleSheets: Chocolate Midnight Modernist Oldstyle Steely Swiss

2、 Traditional Ultramarine * This document is written in multilingual format. We strongly suggest that you choose your language first to get a better display. piaips Using (lib)SVM Tutorial piaip 的 (lib)SVM 簡易入門 piaip at csie dot ntu dot edu dot tw, Hung-Te LinFri Apr 18 15:04:53 CST 2003 $Id: svm_tut

3、orial.html,v 1.13 2007/10/02 05:51:55 piaip Exp piaip $ 原作：林弘德，轉載請保留原出處 Why this tutorial is here 我一直覺得 SVM 是個很有趣的東西，不過也一直沒辦法 (mostly 衝堂) 去聽林智仁老師 的 Data mining 跟 SVM 的課； 後來看了一些網路上的文件跟聽 kcwu 講了一下 libsvm 的用法後，就想整理一下，算是對於並不需要知道完整 SVM 理論的人提供使用 libsvm 的入門。 原始 libsvm 的 README 跟 FAQ 也是很好的文件， 不過你可能要先對 svm 跟

4、流程有點了解才看得懂 (我在看時有這樣的感覺)； 這篇入門就是為了從零開始的人而寫的。 Ive been considering SVM as an interesting and useful tool but couldnt attend the Data mining and SVM course by prof. cjline about it (mostly due to scheduling conflicts). After reading some materials on the internet and discussing libsvm with some of my c

5、lassmates and friends , I wanted to provide some notes here as a tutorial for those who do not need to know the complete theory behind SVM theory to use libsvm . The original README and FAQ files that comes with libsvm are good documents too. But you may need to have some basic knowledge of SVM and

6、its workflow (thats how I felt when I was reading them). This tutorial is specificly for those starting from zero. 後來還有一些人提供意見，所以在此要感謝： I must thank these guys who provided feedback and helped me make this tutorial: kcwu, biboshen, puffer, somi 不過請記得底下可能有些說法不一定對，但是對於只是想用 SVM 的人來說我覺得這樣說明會比較易懂。 Rememb

7、er that some aspect below may not be correct. But for those who just wish to USE SVM, I think the explanation below is easier to understand. 這篇入門原則上是給會寫基本程式的人看的，也是給我自己一個備忘, 不用太多數學底子，也不用對 SVM 有任何先備知識。 This tutorial is basically for people who already know how to program. Its also a memo to myself. Ne

8、ither too much mathmatics nor prior SVM knowledge is required. 還看不懂的話有三個情形, 一是我講的不夠清楚, 二是你的常識不足, 三是你是小白 ; If you still cant understand this tutorial, there are three possibilities: 1. I didnt explain clearly enough, 2. You lack sufficient common knowledge, 3. You dont use your brain properly ; 我自己是以

9、完全不懂的角度開始的，這篇入門也有不少一樣不懂 SVM 的人 看過、而且看完多半都有一定程度的理解，所以假設情況一不會發生， 那如果不懂一定是後兩個情況 :P 也所以, 有問題別問我。 Since I begin writing this myself with no understanding of the subject, ans this document has been read by many people who also didnt understand SVM but gained a certain level of understanding after reading

10、it, possibility 1 can be ruled out. Thus if you cant understand it you must belong to the latter two categories, :P thus even if you have any questions after reading this, dont ask me. SVM: What is it and what can it do for me? SVM, Support Vector Machine , 簡而言之它是個起源跟類神經網路有點像的東西， 不過現今最常拿來就是做分類 (clas

11、sification)。 也就是說，如果我有一堆已經分好類的東西 （可是分類的依據是未知的！） ，那當收到新的東西時， SVM 可以預測 (predict) 新的資料要分到哪一堆去。 SVM, Support Vector Machine , is something that has similar roots with neural networks. But recently it has been widely used in Classification. That means, if I have some sets of things classified (But you kn

12、ow nothing about HOW I CLASSIFIED THEM, or say you dont know the rules used for classification), when a new data comes, SVM can PREDICT which set it should belong to. 聽起來是很神奇的事（如果你覺得不神奇，請重想一想這句話代表什麼： 分類的依據是未知的！，還是不神奇的話就請你寫個程式 解解看這個問題）， 也很像要 AI 之類的高等技巧. 不過 SVM 基於 統計學習理論 可以在合理的時間內漂亮的解決這個問題。 It sounds

13、marvelous and would seem to require advanced techniques like AI searching or some time-consuming complex computation. But SVM used some Statistical Learning Theory to solve this problem in reasonable time. 以圖形化的例子來說明(by SVMToy), 像假定我在空間中標了一堆用顏色分類的點, 點的顏色就是他的類別, 位置就是他的資料, 那 SVM 就可以找出區隔這些點的方程式, 依此就可以分

14、出一區區的區域; 拿到新的點(資料) 時, 只要對照該位置在哪一區就可以(predict) 找出他應該是哪一顏色(類別)了: Now we explain with a graphical example(by SVMToy), I marked lots of points with different colors on a plane, the color of each point is its class and the location is its data. SVM can then find equations to split these points and with t

15、hese equations we can get colored regions. When a new point(data) comes, we can find (predict) what color (class) a point should be just by using the points location (data) 原始資料分佈 Original DataSVM找出來的區域 SVM Regions 當然 SVM 不是真的只有畫圖分區那麼簡單, 不過看上面的例子應該可以了解 SVM 大概在作什麼. Of course SVM is not really just ab

16、out painting and marking regions, but with the example above you should should be able to get some idea about what SVM is doing. 要對 SVM 再多懂一點點，可以參考 cjlin 在 data mining 課的 slides: pdf or ps 。 底下我試著在不用看那個 slide 的情況 解釋及使用 libsvm。 To get yourself more familiar with SVM, you may refer to the slides cjlin used in his Data Mining course : pdf or ps .Im going to try to explain and use libS